Overview

Dataset statistics

Number of variables28
Number of observations17481
Missing cells17942
Missing cells (%)3.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 MiB
Average record size in memory224.0 B

Variable types

NUM16
CAT10
UNSUPPORTED1
BOOL1

Reproduction

Analysis started2020-08-11 00:44:57.143715
Analysis finished2020-08-11 00:46:04.279553
Duration1 minute and 7.14 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

username has a high cardinality: 10490 distinct values High cardinality
tweet has a high cardinality: 16893 distinct values High cardinality
stopwords has a high cardinality: 14801 distinct values High cardinality
clean_text has a high cardinality: 16317 distinct values High cardinality
Start Date has a high cardinality: 67 distinct values High cardinality
End Date has a high cardinality: 67 distinct values High cardinality
Sample has a high cardinality: 69 distinct values High cardinality
likes_count is highly correlated with retweets_countHigh correlation
retweets_count is highly correlated with likes_countHigh correlation
positive is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
Unnamed: 0 is highly correlated with positiveHigh correlation
date is highly correlated with positive and 1 other fieldsHigh correlation
death is highly correlated with date and 1 other fieldsHigh correlation
End Date is highly correlated with Poll and 3 other fieldsHigh correlation
Poll is highly correlated with End Date and 1 other fieldsHigh correlation
Start Date is highly correlated with End Date and 2 other fieldsHigh correlation
Sample is highly correlated with Poll and 3 other fieldsHigh correlation
MoE is highly correlated with Start Date and 2 other fieldsHigh correlation
geo has 17481 (100.0%) missing values Missing
death has 453 (2.6%) missing values Missing
tweet is uniformly distributed Uniform
clean_text is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
geo is an unsupported type, check if it needs cleaning or further analysis Unsupported
replies_count has 10882 (62.3%) zeros Zeros
retweets_count has 11418 (65.3%) zeros Zeros
likes_count has 8671 (49.6%) zeros Zeros
stopwords_count has 683 (3.9%) zeros Zeros
Sentiment has 4440 (25.4%) zeros Zeros
Topic has 1130 (6.5%) zeros Zeros

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count17481
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8740.0
Minimum0
Maximum17480
Zeros1
Zeros (%)< 0.1%
Memory size136.6 KiB
2020-08-10T20:46:04.438881image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile874
Q14370
median8740
Q313110
95-th percentile16606
Maximum17480
Range17480
Interquartile range (IQR)8740

Descriptive statistics

Standard deviation5046.474363
Coefficient of variation (CV)0.5773998127
Kurtosis-1.2
Mean8740
Median Absolute Deviation (MAD)4370
Skewness0
Sum152783940
Variance25466903.5
2020-08-10T20:46:04.699085image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20471< 0.1%
 
47431< 0.1%
 
88331< 0.1%
 
149781< 0.1%
 
129311< 0.1%
 
26921< 0.1%
 
6451< 0.1%
 
67901< 0.1%
 
170371< 0.1%
 
7251< 0.1%
 
Other values (17471)1747199.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
174801< 0.1%
 
174791< 0.1%
 
174781< 0.1%
 
174771< 0.1%
 
174761< 0.1%
 

date
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count198
Unique (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.593644095417882e+18
Minimum1579651200000000000
Maximum1596672000000000000
Zeros0
Zeros (%)0.0%
Memory size136.6 KiB
2020-08-10T20:46:04.901290image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1.5796512e+18
5-th percentile1.5848352e+18
Q11.5937344e+18
median1.5945984e+18
Q31.5955488e+18
95-th percentile1.5964128e+18
Maximum1.596672e+18
Range1.70208e+16
Interquartile range (IQR)1.8144e+15

Descriptive statistics

Standard deviation3.422107929e+15
Coefficient of variation (CV)0.002147347666
Kurtosis4.790678816
Mean1.593644095e+18
Median Absolute Deviation (MAD)8.64e+14
Skewness-2.291196015
Sum3.908880699e+18
Variance1.171082268e+31
2020-08-10T20:46:05.108307image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.5945984e+185913.4%
 
1.5939936e+185493.1%
 
1.5941664e+185142.9%
 
1.59408e+184872.8%
 
1.594944e+184842.8%
 
1.5948576e+184552.6%
 
1.5943392e+184502.6%
 
1.593648e+184492.6%
 
1.5960672e+184302.5%
 
1.5942528e+184242.4%
 
Other values (188)1264872.4%
 
ValueCountFrequency (%) 
1.5796512e+18170.1%
 
1.5797376e+18250.1%
 
1.579824e+18160.1%
 
1.5799104e+18160.1%
 
1.5799968e+18180.1%
 
ValueCountFrequency (%) 
1.596672e+182251.3%
 
1.5965856e+182751.6%
 
1.5964992e+183351.9%
 
1.5964128e+183672.1%
 
1.5963264e+183321.9%
 

username
Categorical

HIGH CARDINALITY

Distinct count10490
Unique (%)60.0%
Missing0
Missing (%)0.0%
Memory size136.6 KiB
realdonaldtrump
 
2541
washingtonpost
 
576
nytimes
 
267
freddiesirmans
 
160
bornwildm
 
116
Other values (10485)
13821
ValueCountFrequency (%) 
realdonaldtrump254114.5%
 
washingtonpost5763.3%
 
nytimes2671.5%
 
freddiesirmans1600.9%
 
bornwildm1160.7%
 
democratboricua940.5%
 
nygovcuomo830.5%
 
davidhamer_1951590.3%
 
sudiptamalakar4460.3%
 
ykhalim450.3%
 
Other values (10480)1349477.2%
 
2020-08-10T20:46:05.493303image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length15
Median length12
Mean length11.84285796
Min length2

tweet
Categorical

HIGH CARDINALITY
UNIFORM

Distinct count16893
Unique (%)96.6%
Missing0
Missing (%)0.0%
Memory size136.6 KiB
Time for #USA to default on all the bonds held by #China to pay for spreading #Covid_19 #COVID19 and causing trillions in damage to life and global economy. @realDonaldTrump @POTUS @SecPompeo #trump #usa #USCHINA #SouthChinaSea #Taiwan #HongKong #Vietnam #Huawei #BoycottChinese
 
25
#CHRIST is here since 1998 – to take back the earth. And Plagues, pestilence, and assaults, like Covid-19 will be used to destroy the USA and the 8th World Empire – and create God’s Kingdom on Earth. Daniel 2:32-45.
 
24
Something to think about: according to the WHO website on COVID-19 France and the UK have a death rate of over 18% if you have the virus! USA death rate is less than 4%!! That you won't hear from CDC, Fauci and the FAKE NEWS MEDIA!!!
 
21
COVID-19 and other measures FINISH THE USA & 8th World Empire off! I have told you & showed you – but you are too stupid to see what is right there before your eyes. The “Mirror” & events show you, narration tells you. Nature has a remedy for you. Daniel 2:32-45.
 
20
Banana republic COVID-19 cases in Canada are down 82% from their peak. COVID-19 cases in Italy are down 97% from their peak. COVID-19 cases in New Zealand are down 100% from their peak. COVID-19 cases in the USA are higher than ever and going up fast. #TrumpVirus
 
17
Other values (16888)
17374
ValueCountFrequency (%) 
Time for #USA to default on all the bonds held by #China to pay for spreading #Covid_19 #COVID19 and causing trillions in damage to life and global economy. @realDonaldTrump @POTUS @SecPompeo #trump #usa #USCHINA #SouthChinaSea #Taiwan #HongKong #Vietnam #Huawei #BoycottChinese250.1%
 
#CHRIST is here since 1998 – to take back the earth. And Plagues, pestilence, and assaults, like Covid-19 will be used to destroy the USA and the 8th World Empire – and create God’s Kingdom on Earth. Daniel 2:32-45.240.1%
 
Something to think about: according to the WHO website on COVID-19 France and the UK have a death rate of over 18% if you have the virus! USA death rate is less than 4%!! That you won't hear from CDC, Fauci and the FAKE NEWS MEDIA!!!210.1%
 
COVID-19 and other measures FINISH THE USA & 8th World Empire off! I have told you & showed you – but you are too stupid to see what is right there before your eyes. The “Mirror” & events show you, narration tells you. Nature has a remedy for you. Daniel 2:32-45.200.1%
 
Banana republic COVID-19 cases in Canada are down 82% from their peak. COVID-19 cases in Italy are down 97% from their peak. COVID-19 cases in New Zealand are down 100% from their peak. COVID-19 cases in the USA are higher than ever and going up fast. #TrumpVirus170.1%
 
Well whatever anyone says about COVID-19, it is clearly NOT working in the USA, all thanks to your amazing president who prefers to deal with the deregulation of dishwashers! The USA's leaders behavior is beneath human dignity and inexcusable. How many more must die?150.1%
 
45 maybe best friend is covid 19 and maybe russia are north korea are iran china take part in a way it help 45? 45 think it might could help him win again he knew it was on the way why was he so slow he call it a hoax well 45 cant fool usa people my own opinion.130.1%
 
Huge DISCOUNT FIRST Time ever in Amazon History for Covid-19 and Independence day 2020 !!! Happy Learning !!! eBook on Amazon USA: https://amzn.to/2GOXJDD  eBook on Amazon India: https://amzn.to/3b7yr1F 130.1%
 
Huge DISCOUNT FIRST Time ever in Amazon History for Covid-19 and Independence day 2020 !!! Happy Learning !!! eBook on Amazon USA: https://bit.ly/2RPuKWO  eBook on Amazon India: https://bit.ly/2u7PY92 120.1%
 
LAW & ORDER!120.1%
 
Other values (16883)1730999.0%
 
2020-08-10T20:46:05.891457image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length1110
Median length255
Mean length233.9806647
Min length10

replies_count
Real number (ℝ≥0)

ZEROS

Distinct count2681
Unique (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2770.280018305589
Minimum0
Maximum193481
Zeros10882
Zeros (%)62.3%
Memory size136.6 KiB
2020-08-10T20:46:06.069529image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile18571
Maximum193481
Range193481
Interquartile range (IQR)2

Descriptive statistics

Standard deviation10481.30692
Coefficient of variation (CV)3.783482844
Kurtosis61.3258946
Mean2770.280018
Median Absolute Deviation (MAD)0
Skewness6.51795077
Sum48427265
Variance109857794.8
2020-08-10T20:46:06.259741image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01088262.3%
 
1220912.6%
 
24932.8%
 
31741.0%
 
4680.4%
 
5580.3%
 
6350.2%
 
7320.2%
 
12300.2%
 
13300.2%
 
Other values (2671)347019.9%
 
ValueCountFrequency (%) 
01088262.3%
 
1220912.6%
 
24932.8%
 
31741.0%
 
4680.4%
 
ValueCountFrequency (%) 
1934811< 0.1%
 
1911331< 0.1%
 
1877051< 0.1%
 
1717771< 0.1%
 
1692761< 0.1%
 

retweets_count
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count2908
Unique (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3567.0496539099595
Minimum0
Maximum216656
Zeros11418
Zeros (%)65.3%
Memory size136.6 KiB
2020-08-10T20:46:06.467432image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile26032
Maximum216656
Range216656
Interquartile range (IQR)2

Descriptive statistics

Standard deviation10643.6198
Coefficient of variation (CV)2.98387206
Kurtosis28.98592413
Mean3567.049654
Median Absolute Deviation (MAD)0
Skewness4.367983486
Sum62355595
Variance113286642.4
2020-08-10T20:46:06.640993image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01141865.3%
 
113437.7%
 
24302.5%
 
32141.2%
 
41270.7%
 
5750.4%
 
6480.3%
 
7440.3%
 
8310.2%
 
10310.2%
 
Other values (2898)372021.3%
 
ValueCountFrequency (%) 
01141865.3%
 
113437.7%
 
24302.5%
 
32141.2%
 
41270.7%
 
ValueCountFrequency (%) 
2166561< 0.1%
 
1171441< 0.1%
 
1117531< 0.1%
 
1109001< 0.1%
 
1085471< 0.1%
 

likes_count
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count3169
Unique (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16247.852640009152
Minimum0
Maximum808801
Zeros8671
Zeros (%)49.6%
Memory size136.6 KiB
2020-08-10T20:46:06.828309image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile115198
Maximum808801
Range808801
Interquartile range (IQR)7

Descriptive statistics

Standard deviation50416.4073
Coefficient of variation (CV)3.102958182
Kurtosis30.60619684
Mean16247.85264
Median Absolute Deviation (MAD)1
Skewness4.693521632
Sum284028712
Variance2541814125
2020-08-10T20:46:07.025755image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0867149.6%
 
1229813.1%
 
29435.4%
 
34682.7%
 
43001.7%
 
52001.1%
 
61480.8%
 
71130.6%
 
8830.5%
 
9800.5%
 
Other values (3159)417723.9%
 
ValueCountFrequency (%) 
0867149.6%
 
1229813.1%
 
29435.4%
 
34682.7%
 
43001.7%
 
ValueCountFrequency (%) 
8088011< 0.1%
 
7078041< 0.1%
 
6202981< 0.1%
 
5811561< 0.1%
 
5610441< 0.1%
 

video
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size136.6 KiB
0
17102
1
 
379
ValueCountFrequency (%) 
01710297.8%
 
13792.2%
 

geo
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing17481
Missing (%)100.0%
Memory size136.7 KiB

positive
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count187
Unique (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3184083.3528402266
Minimum2
Maximum4852143
Zeros0
Zeros (%)0.0%
Memory size136.6 KiB
2020-08-10T20:46:07.218424image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile42169
Q12786467
median3350434
Q34093266
95-th percentile4694126
Maximum4852143
Range4852141
Interquartile range (IQR)1306799

Descriptive statistics

Standard deviation1233987.308
Coefficient of variation (CV)0.3875486824
Kurtosis0.7947422338
Mean3184083.353
Median Absolute Deviation (MAD)618190
Skewness-1.104340122
Sum5.566096109e+10
Variance1.522724676e+12
2020-08-10T20:46:07.399076image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
33504345913.4%
 
29285905493.1%
 
30425035142.9%
 
29803564872.8%
 
36268814842.8%
 
35496484552.6%
 
31679844502.6%
 
27322444492.6%
 
44678524302.5%
 
31013394242.4%
 
Other values (177)1264872.4%
 
ValueCountFrequency (%) 
21200.7%
 
3400.2%
 
48< 0.1%
 
68< 0.1%
 
7110.1%
 
ValueCountFrequency (%) 
48521432251.3%
 
47979592751.6%
 
47456943351.9%
 
46941263672.1%
 
46445653321.9%
 

death
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct count162
Unique (%)1.0%
Missing453
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean121611.92858820765
Minimum2.0
Maximum151483.0
Zeros0
Zeros (%)0.0%
Memory size136.6 KiB
2020-08-10T20:46:07.588024image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile27862
Q1122446
median127909
Q3137602
95-th percentile147631
Maximum151483
Range151481
Interquartile range (IQR)15156

Descriptive statistics

Standard deviation32335.62998
Coefficient of variation (CV)0.2658919265
Kurtosis6.577441345
Mean121611.9286
Median Absolute Deviation (MAD)6714.5
Skewness-2.637883169
Sum2070807920
Variance1045592966
2020-08-10T20:46:07.771150image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1279095913.4%
 
1228985493.1%
 
1246285142.9%
 
1238214872.8%
 
1314014842.8%
 
1304504552.6%
 
1263494502.6%
 
1215424492.6%
 
1441144302.5%
 
1254954242.4%
 
Other values (152)1219569.8%
 
(Missing)4532.6%
 
ValueCountFrequency (%) 
2260.1%
 
4170.1%
 
590.1%
 
8200.1%
 
11280.2%
 
ValueCountFrequency (%) 
1514832251.3%
 
1502322751.6%
 
1488073351.9%
 
1476313672.1%
 
1471123321.9%
 

word_count
Real number (ℝ≥0)

Distinct count74
Unique (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.87180367255878
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Memory size136.6 KiB
2020-08-10T20:46:07.948682image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q127
median38
Q346
95-th percentile52
Maximum91
Range90
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.66401258
Coefficient of variation (CV)0.3530352891
Kurtosis-0.3207170802
Mean35.87180367
Median Absolute Deviation (MAD)9
Skewness-0.5744950938
Sum627075
Variance160.3772146
2020-08-10T20:46:08.111038image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
477014.0%
 
456713.8%
 
466583.8%
 
436553.7%
 
426443.7%
 
445943.4%
 
485903.4%
 
415533.2%
 
385403.1%
 
395393.1%
 
Other values (64)1133664.8%
 
ValueCountFrequency (%) 
14< 0.1%
 
2390.2%
 
3780.4%
 
41100.6%
 
5870.5%
 
ValueCountFrequency (%) 
911< 0.1%
 
901< 0.1%
 
861< 0.1%
 
831< 0.1%
 
811< 0.1%
 

avg_word_length
Real number (ℝ≥0)

Distinct count3807
Unique (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.865800014049402
Minimum3.0526315789473686
Maximum122.11111111111113
Zeros0
Zeros (%)0.0%
Memory size136.6 KiB
2020-08-10T20:46:08.284048image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum3.052631579
5-th percentile4.086956522
Q14.636363636
median5.244444444
Q36.368421053
95-th percentile9.6
Maximum122.1111111
Range119.0584795
Interquartile range (IQR)1.732057416

Descriptive statistics

Standard deviation2.266082755
Coefficient of variation (CV)0.3863211752
Kurtosis409.0140737
Mean5.865800014
Median Absolute Deviation (MAD)0.7444444444
Skewness10.32991592
Sum102540.05
Variance5.135131051
2020-08-10T20:46:08.450450image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
51701.0%
 
61320.8%
 
4.51010.6%
 
4.6880.5%
 
4830.5%
 
5.5810.5%
 
7730.4%
 
4.666666667690.4%
 
4.833333333690.4%
 
4.75670.4%
 
Other values (3797)1654894.7%
 
ValueCountFrequency (%) 
3.0526315791< 0.1%
 
3.1463414631< 0.1%
 
3.1538461541< 0.1%
 
3.21< 0.1%
 
3.2131147541< 0.1%
 
ValueCountFrequency (%) 
122.11111111< 0.1%
 
361< 0.1%
 
31.752< 0.1%
 
27.428571431< 0.1%
 
24.666666671< 0.1%
 

stopwords_count
Real number (ℝ≥0)

ZEROS

Distinct count36
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.270064641610892
Minimum0
Maximum36
Zeros683
Zeros (%)3.9%
Memory size136.6 KiB
2020-08-10T20:46:08.623718image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median13
Q317
95-th percentile23
Maximum36
Range36
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.595357673
Coefficient of variation (CV)0.537516131
Kurtosis-0.6328511159
Mean12.27006464
Median Absolute Deviation (MAD)5
Skewness0.03848289588
Sum214493
Variance43.49874284
2020-08-10T20:46:08.809050image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1510025.7%
 
139875.6%
 
149585.5%
 
129425.4%
 
179325.3%
 
169105.2%
 
108634.9%
 
118384.8%
 
188064.6%
 
77774.4%
 
Other values (26)846648.4%
 
ValueCountFrequency (%) 
06833.9%
 
13071.8%
 
24322.5%
 
35193.0%
 
46113.5%
 
ValueCountFrequency (%) 
361< 0.1%
 
352< 0.1%
 
334< 0.1%
 
327< 0.1%
 
31100.1%
 

char_count
Real number (ℝ≥0)

Distinct count457
Unique (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233.98066472169785
Minimum10
Maximum1110
Zeros0
Zeros (%)0.0%
Memory size136.6 KiB
2020-08-10T20:46:09.000715image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile93
Q1186
median255
Q3279
95-th percentile339
Maximum1110
Range1100
Interquartile range (IQR)93

Descriptive statistics

Standard deviation75.58009128
Coefficient of variation (CV)0.3230185339
Kurtosis1.168054904
Mean233.9806647
Median Absolute Deviation (MAD)40
Skewness-0.3282054299
Sum4090216
Variance5712.350198
2020-08-10T20:46:09.171562image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2807564.3%
 
2796673.8%
 
2784762.7%
 
2773612.1%
 
2763111.8%
 
2752551.5%
 
2742351.3%
 
2731881.1%
 
2711841.1%
 
2721821.0%
 
Other values (447)1386679.3%
 
ValueCountFrequency (%) 
105< 0.1%
 
111< 0.1%
 
12120.1%
 
133< 0.1%
 
163< 0.1%
 
ValueCountFrequency (%) 
11101< 0.1%
 
6021< 0.1%
 
6011< 0.1%
 
5951< 0.1%
 
5941< 0.1%
 

stopwords
Categorical

HIGH CARDINALITY

Distinct count14801
Unique (%)84.7%
Missing0
Missing (%)0.0%
Memory size136.6 KiB
[]
 
683
['and']
 
112
['of', 'of', 'and', 'of', 'and', 'in', 'below']
 
51
['in', 'for', 'and', 'on', 'on']
 
47
['you']
 
43
Other values (14796)
16545
ValueCountFrequency (%) 
[]6833.9%
 
['and']1120.6%
 
['of', 'of', 'and', 'of', 'and', 'in', 'below']510.3%
 
['in', 'for', 'and', 'on', 'on']470.3%
 
['you']430.2%
 
['for', 'to', 'on', 'all', 'the', 'by', 'to', 'for', 'and', 'in', 'to', 'and']340.2%
 
['to', 'and', 'for']340.2%
 
['into', 'than', 'and', 'is', 'more', 'the', 'of', 'the']320.2%
 
['of', 'at', 'is', 'than', 'and', 'is', 'at']310.2%
 
['just', 'our', 'do', 'you', 'is', 'it', 'the', 'just', 'that', 'you', 'the', 'to', 'through', 'this', 'but', 'then']280.2%
 
Other values (14791)1638693.7%
 
2020-08-10T20:46:09.545392image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length242
Median length86
Mean length84.70791145
Min length2

clean_text
Categorical

HIGH CARDINALITY
UNIFORM

Distinct count16317
Unique (%)93.4%
Missing8
Missing (%)< 0.1%
Memory size136.6 KiB
huge discount first time ever amazon history covid independence happy learning ebook amazon ebook amazon india
 
47
julycoronavirus covid status total increase confirmed cases death test hospitalisation worldwide state newyorkcity please detail supporting reports facebook link
 
39
time default bonds held china spreading covid_ covid causing trillions damage life global economy realdonaldtrump potus secpompeo trump uschina southchinasea taiwan hongkong vietnam huawei boycottchinese
 
32
heads lets minds together think nothing democrats whyre good like know help covid nasty president sorry want yall safe
 
28
corner covid experience deaths million population lower belgium spain italy sweden france netherlands ireland
 
28
Other values (16312)
17299
ValueCountFrequency (%) 
huge discount first time ever amazon history covid independence happy learning ebook amazon ebook amazon india470.3%
 
julycoronavirus covid status total increase confirmed cases death test hospitalisation worldwide state newyorkcity please detail supporting reports facebook link390.2%
 
time default bonds held china spreading covid_ covid causing trillions damage life global economy realdonaldtrump potus secpompeo trump uschina southchinasea taiwan hongkong vietnam huawei boycottchinese320.2%
 
heads lets minds together think nothing democrats whyre good like know help covid nasty president sorry want yall safe280.2%
 
corner covid experience deaths million population lower belgium spain italy sweden france netherlands ireland280.2%
 
christ since take back earth plagues pestilence assaults like covid used destroy world empire create gods kingdom earth daniel240.1%
 
something think according website covid france death rate virus death rate less wont hear fauci fake news media210.1%
 
covid measures finish world empire told showed stupid right eyes mirror events show narration tells nature remedy daniel200.1%
 
terms covid cases canadas experience million lower sweden spain iceland belgium ireland portugal italy switzerland netherlands190.1%
 
thank190.1%
 
Other values (16307)1719698.4%
 
2020-08-10T20:46:09.915929image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length251
Median length137
Mean length129.2029632
Min length3

Sentiment
Real number (ℝ)

ZEROS

Distinct count2402
Unique (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05259328353233437
Minimum-1.0
Maximum1.0
Zeros4440
Zeros (%)25.4%
Memory size136.6 KiB
2020-08-10T20:46:10.106409image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.4
Q1-0.05436507937
median0
Q30.2
95-th percentile0.5
Maximum1
Range2
Interquartile range (IQR)0.2543650794

Descriptive statistics

Standard deviation0.2759058896
Coefficient of variation (CV)5.246028981
Kurtosis1.813930034
Mean0.05259328353
Median Absolute Deviation (MAD)0.1333333333
Skewness0.05193317762
Sum919.3831894
Variance0.07612405991
2020-08-10T20:46:10.298241image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0444025.4%
 
0.54222.4%
 
0.24182.4%
 
0.253812.2%
 
-0.23071.8%
 
0.12961.7%
 
-0.12791.6%
 
0.42571.5%
 
-0.52411.4%
 
0.82231.3%
 
Other values (2392)1021758.4%
 
ValueCountFrequency (%) 
-1760.4%
 
-0.96< 0.1%
 
-0.91< 0.1%
 
-0.8752< 0.1%
 
-0.86666666671< 0.1%
 
ValueCountFrequency (%) 
1710.4%
 
0.93333333331< 0.1%
 
0.9251< 0.1%
 
0.9200.1%
 
0.8751< 0.1%
 

Poll
Categorical

HIGH CORRELATION

Distinct count20
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size136.6 KiB
Economist/YouGovYouGov
5196
The Hill/HarrisXThe Hill
2944
RCP Average
1553
EmersonEmerson
1245
QuinnipiacQuinnipiac
1180
Other values (15)
5363
ValueCountFrequency (%) 
Economist/YouGovYouGov519629.7%
 
The Hill/HarrisXThe Hill294416.8%
 
RCP Average15538.9%
 
EmersonEmerson12457.1%
 
QuinnipiacQuinnipiac11806.8%
 
CBS News/YouGovCBS News11436.5%
 
FOX NewsFOX News10105.8%
 
Rasmussen ReportsRasmussen9575.5%
 
NBC News/Wall St. JrnlNBC/WSJ5823.3%
 
IBD/TIPPIBD/TIPP5022.9%
 
Other values (10)11696.7%
 
2020-08-10T20:46:10.650894image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length29
Median length22
Mean length20.37606544
Min length6

Start Date
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct count67
Unique (%)0.4%
Missing0
Missing (%)0.0%
Memory size136.6 KiB
2020-07-09
2603
2020-07-05
1875
2020-07-29
1780
2020-06-28
1664
2020-07-21
 
1392
Other values (62)
8167
ValueCountFrequency (%) 
2020-07-09260314.9%
 
2020-07-05187510.7%
 
2020-07-29178010.2%
 
2020-06-2816649.5%
 
2020-07-2113928.0%
 
2020-07-0312567.2%
 
2020-07-128374.8%
 
2020-08-028354.8%
 
2020-07-177594.3%
 
2020-07-197234.1%
 
Other values (57)375721.5%
 
2020-08-10T20:46:10.968264image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

End Date
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct count67
Unique (%)0.4%
Missing0
Missing (%)0.0%
Memory size136.6 KiB
2020-07-21
1933
2020-07-07
 
1875
2020-06-30
 
1675
2020-07-04
 
1256
2020-08-05
 
1178
Other values (62)
9564
ValueCountFrequency (%) 
2020-07-21193311.1%
 
2020-07-07187510.7%
 
2020-06-3016759.6%
 
2020-07-0412567.2%
 
2020-08-0511786.7%
 
2020-07-3010816.2%
 
2020-07-2410496.0%
 
2020-07-1310135.8%
 
2020-07-288544.9%
 
2020-07-158374.8%
 
Other values (57)473027.1%
 
2020-08-10T20:46:11.295536image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Sample
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct count69
Unique (%)0.4%
Missing0
Missing (%)0.0%
Memory size136.6 KiB
1165 RV
 
1875
1198 RV
 
1664
--
 
1553
933 RV
 
1256
964 LV
 
1081
Other values (64)
10052
ValueCountFrequency (%) 
1165 RV187510.7%
 
1198 RV16649.5%
 
--15538.9%
 
933 RV12567.2%
 
964 LV10816.2%
 
1401 LV10496.0%
 
1273 RV10135.8%
 
2500 LV9575.5%
 
1104 RV8374.8%
 
2850 RV8354.8%
 
Other values (59)536130.7%
 
2020-08-10T20:46:11.625819image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.319489732
Min length2

MoE
Categorical

HIGH CORRELATION

Distinct count20
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size136.6 KiB
--
2910
3.4
2232
3.2
2161
3.6
2100
1.8
1594
Other values (15)
6484
ValueCountFrequency (%) 
--291016.6%
 
3.4223212.8%
 
3.2216112.4%
 
3.6210012.0%
 
1.815949.1%
 
3.115068.6%
 
2.811576.6%
 
310946.3%
 
210175.8%
 
3.37604.3%
 
Other values (10)9505.4%
 
2020-08-10T20:46:11.963854image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.575996797
Min length1

Biden (D)
Real number (ℝ≥0)

Distinct count15
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.567719238029866
Minimum42.0
Maximum56.0
Zeros0
Zeros (%)0.0%
Memory size136.6 KiB
2020-08-10T20:46:12.162467image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile43
Q148
median49
Q350
95-th percentile52
Maximum56
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.762877214
Coefficient of variation (CV)0.05688711057
Kurtosis0.3932028498
Mean48.56771924
Median Absolute Deviation (MAD)1
Skewness-0.5818357514
Sum849012.3
Variance7.633490501
2020-08-10T20:46:12.345067image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
49498728.5%
 
48258314.8%
 
43213612.2%
 
5015999.1%
 
5115458.8%
 
49.612106.9%
 
5212076.9%
 
458394.8%
 
553482.0%
 
49.13432.0%
 
Other values (5)6843.9%
 
ValueCountFrequency (%) 
42770.4%
 
43213612.2%
 
458394.8%
 
46730.4%
 
472581.5%
 
ValueCountFrequency (%) 
56700.4%
 
553482.0%
 
532061.2%
 
5212076.9%
 
5115458.8%
 

Trump (R)
Real number (ℝ≥0)

Distinct count17
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.07357130598936
Minimum36.0
Maximum52.0
Zeros0
Zeros (%)0.0%
Memory size136.6 KiB
2020-08-10T20:46:12.535886image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile37
Q140
median40.9
Q341
95-th percentile46
Maximum52
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.474917075
Coefficient of variation (CV)0.06025570692
Kurtosis1.671302517
Mean41.07357131
Median Absolute Deviation (MAD)0.9
Skewness1.088993899
Sum718007.1
Variance6.125214528
2020-08-10T20:46:12.717033image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
40532630.5%
 
41350820.1%
 
3912737.3%
 
40.912106.9%
 
4612056.9%
 
4510285.9%
 
3710135.8%
 
387764.4%
 
426473.7%
 
435783.3%
 
Other values (7)9175.2%
 
ValueCountFrequency (%) 
36100.1%
 
3710135.8%
 
387764.4%
 
3912737.3%
 
40532630.5%
 
ValueCountFrequency (%) 
52710.4%
 
50750.4%
 
48140.1%
 
471400.8%
 
4612056.9%
 

Spread
Real number (ℝ)

Distinct count18
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.494147932040501
Minimum-4.0
Maximum15.0
Zeros103
Zeros (%)0.6%
Memory size136.6 KiB
2020-08-10T20:46:12.902731image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile3
Q14
median8
Q39
95-th percentile15
Maximum15
Range19
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.22160924
Coefficient of variation (CV)0.4298833262
Kurtosis0.4642815613
Mean7.494147932
Median Absolute Deviation (MAD)2
Skewness0.129853082
Sum131005.2
Variance10.37876609
2020-08-10T20:46:13.071359image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
9398722.8%
 
4260514.9%
 
7196211.2%
 
317209.8%
 
1014978.6%
 
8.712106.9%
 
811866.8%
 
1510135.8%
 
68644.9%
 
114342.5%
 
Other values (8)10035.7%
 
ValueCountFrequency (%) 
-4710.4%
 
01030.6%
 
1730.4%
 
2840.5%
 
317209.8%
 
ValueCountFrequency (%) 
1510135.8%
 
141420.8%
 
12850.5%
 
114342.5%
 
1014978.6%
 

Topic
Real number (ℝ≥0)

ZEROS

Distinct count10
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.913391682398032
Minimum0
Maximum9
Zeros1130
Zeros (%)6.5%
Memory size136.6 KiB
2020-08-10T20:46:13.259134image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.684186199
Coefficient of variation (CV)0.5463000657
Kurtosis-1.082965326
Mean4.913391682
Median Absolute Deviation (MAD)2
Skewness-0.1696933451
Sum85891
Variance7.20485555
2020-08-10T20:46:13.443301image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3351520.1%
 
8291616.7%
 
5232113.3%
 
6213812.2%
 
913978.0%
 
713467.7%
 
112667.2%
 
011306.5%
 
27424.2%
 
47104.1%
 
ValueCountFrequency (%) 
011306.5%
 
112667.2%
 
27424.2%
 
3351520.1%
 
47104.1%
 
ValueCountFrequency (%) 
913978.0%
 
8291616.7%
 
713467.7%
 
6213812.2%
 
5232113.3%
 

Target
Categorical

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size136.6 KiB
Bad Response
11858
Good Response
5623
ValueCountFrequency (%) 
Bad Response1185867.8%
 
Good Response562332.2%
 
2020-08-10T20:46:13.929398image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.32166352
Min length12

Interactions

2020-08-10T20:45:10.829116image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:11.044009image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:11.244487image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:11.502070image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:11.717058image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:11.933193image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:12.123715image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:12.319260image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:12.500712image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:12.692006image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:12.888209image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:13.075739image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:13.282019image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:13.476223image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:13.677717image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:13.872590image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:14.053302image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:14.256705image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:14.462643image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:14.671939image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:14.862890image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:15.076441image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:15.270392image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:15.475094image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:15.664870image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:15.867888image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:16.075828image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:16.415498image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:16.643100image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:16.845762image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:17.051089image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:17.263382image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:17.450054image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:17.655348image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:17.865828image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:18.079395image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:18.270163image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:18.481119image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:18.674717image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:18.889199image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:19.078586image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:19.277299image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:19.479477image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-08-10T20:45:59.727732image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:45:59.903994image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:46:00.073886image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:46:00.256256image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:46:00.430171image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:46:00.609221image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:46:00.786679image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-08-10T20:46:14.150739image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-10T20:46:14.590744image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-10T20:46:14.986491image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-10T20:46:15.405930image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-08-10T20:46:15.831328image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-08-10T20:46:01.293912image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:46:02.838712image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:46:03.637297image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-10T20:46:03.901147image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

Unnamed: 0dateusernametweetreplies_countretweets_countlikes_countvideogeopositivedeathword_countavg_word_lengthstopwords_countchar_countstopwordsclean_textSentimentPollStart DateEnd DateSampleMoEBiden (D)Trump (R)SpreadTopicTarget
001579651200000000000realdonaldtrumpMaking great progress in @Davos. Tremendous numbers of companies will be coming, or returning, to the USA. Hottest Economy! JOBS, JOBS, JOBS!946517624882250NaN2NaN225.4545457141['in', 'of', 'will', 'be', 'or', 'to', 'the']making great progress davos tremendous numbers companies coming returning hottest economy jobs jobs jobs0.566667ABC News/Wash PostABC/WP2020-01-202020-01-23880 RV450.046.04.00Bad Response
111579651200000000000realdonaldtrumpSorry, if you come you will be immediately sent back! https://twitter.com/DHSgov/status/1220103171403665410 …864324619989600NaN2NaN118.1666675109['if', 'you', 'you', 'will', 'be']sorry come immediately sent back-0.250000ABC News/Wash PostABC/WP2020-01-202020-01-23880 RV450.046.04.03Bad Response
221579651200000000000realdonaldtrumpSee you on Friday...Big Crowd! https://twitter.com/March_for_Life/status/1091025377932263427 …703524342975130NaN2NaN612.571429294['you', 'on']fridaybig crowd0.000000ABC News/Wash PostABC/WP2020-01-202020-01-23880 RV450.046.04.00Bad Response
331579651200000000000realdonaldtrumpTrue! https://twitter.com/RandPaul/status/1220044346373877761 …343612031506050NaN2NaN220.333333063[]true0.350000ABC News/Wash PostABC/WP2020-01-202020-01-23880 RV450.046.04.09Good Response
441579651200000000000realdonaldtrump“NO PRESSURE”18086198991224080NaN2NaN26.000000013[]pressure0.000000ABC News/Wash PostABC/WP2020-01-202020-01-23880 RV450.046.04.05Bad Response
551579651200000000000realdonaldtrumpWill be Great! https://twitter.com/WhiteHouse/status/1219708789957578752 …22288103395270NaN2NaN414.000000174['be']great0.800000ABC News/Wash PostABC/WP2020-01-202020-01-23880 RV450.046.04.03Bad Response
661579651200000000000realdonaldtrumpGreat working with you Maria! https://twitter.com/MariaBartiromo/status/1219736663930417155 …17777588364980NaN2NaN612.428571293['with', 'you']great working maria0.800000ABC News/Wash PostABC/WP2020-01-202020-01-23880 RV450.046.04.01Bad Response
771579651200000000000realdonaldtrumpOne of the many great things about our just signed giant Trade Deal with China is that it will bring both the USA & China closer together in so many other ways. Terrific working with President Xi, a man who truly loves his country. Much more to come!8460194731025750NaN2NaN484.22916721250['of', 'the', 'about', 'our', 'just', 'with', 'is', 'that', 'it', 'will', 'both', 'the', 'in', 'so', 'other', 'with', 'a', 'who', 'his', 'more', 'to']many great things signed giant trade deal china bring china closer together many ways terrific working president truly loves country much come0.333333ABC News/Wash PostABC/WP2020-01-202020-01-23880 RV450.046.04.05Bad Response
881579651200000000000realdonaldtrumpWill be interviewed at 5:00 A.M. Eastern by @JoeSquawk on @CNBC at the World Economic Forum in Davos, Switzerland. Enjoy!21164824235720NaN2NaN205.1000007121['be', 'at', 'by', 'on', 'at', 'the', 'in']interviewed eastern joesquawk cnbc world economic forum davos switzerland enjoy0.300000ABC News/Wash PostABC/WP2020-01-202020-01-23880 RV450.046.04.05Bad Response
991579651200000000000realdonaldtrump“Not the Senate’s job to mop up the mess made in the House by the Democrats. Biden admitted that he went to Ukraine and did the Quid Pro Quo.” @SteveScalise @FoxNews1055921869896930NaN2NaN314.35483914165['the', 'to', 'up', 'the', 'in', 'the', 'by', 'the', 'that', 'he', 'to', 'and', 'did', 'the']senates mess made house democrats biden admitted went ukraine quid stevescalise foxnews-0.175000ABC News/Wash PostABC/WP2020-01-202020-01-23880 RV450.046.04.05Bad Response

Last rows

Unnamed: 0dateusernametweetreplies_countretweets_countlikes_countvideogeopositivedeathword_countavg_word_lengthstopwords_countchar_countstopwordsclean_textSentimentPollStart DateEnd DateSampleMoEBiden (D)Trump (R)SpreadTopicTarget
17471174711596672000000000000mat945In times of challenge people become vulnerable and seek strong, proactive, cohesive, bipartisan government to lead them back - recent events in the USA suggest federal and state governments are either unwilling or unable to stand together as one in the fight against COVID-19.0000NaN4852143151483.0445.29545514276['of', 'and', 'to', 'them', 'in', 'the', 'and', 'are', 'or', 'to', 'as', 'in', 'the', 'against']times challenge people become vulnerable seek strong proactive cohesive bipartisan government lead back recent events suggest federal state governments either unwilling unable stand together fight covid-0.113333The Hill/HarrisXThe Hill2020-08-022020-08-052850 RV1.843.040.03.06Good Response
17472174721596672000000000000allangpatersonThe USA will top 5million Covid-19 cases today, what a dark and demeaning statistic for the President.\n https://www.worldometers.info/coronavirus/0010NaN4852143151483.0187.1111116147['will', 'what', 'a', 'and', 'for', 'the']million covid cases today dark demeaning statistic president-0.150000The Hill/HarrisXThe Hill2020-08-022020-08-052850 RV1.843.040.03.06Good Response
17473174731596672000000000000carolynguzziLike opposite Day in high school! This is all a political ploy and after the election it’s all over with the COVID-19 will not be either existing or it will be described as a Sara 2 we have a cure for Covid it’s called HYDROQUOROQUIN and z pac u can get this in USA and it is safe https://twitter.com/jim_jordan/status/1291347950753525761 …0000NaN4852143151483.0604.68333331341['in', 'is', 'all', 'a', 'and', 'after', 'the', 'all', 'over', 'with', 'the', 'will', 'not', 'be', 'or', 'it', 'will', 'be', 'as', 'a', 'we', 'have', 'a', 'for', 'and', 'can', 'this', 'in', 'and', 'it', 'is']like opposite high school political ploy election covid either existing described sara cure covid called hydroquoroquin safe0.165000The Hill/HarrisXThe Hill2020-08-022020-08-052850 RV1.843.040.03.06Good Response
17474174741596672000000000000pppjainModiji Should be honoured With Covid Memorial prize for Highest Covid Cases in India and helping 150 countries in fighting Corona. He did not fight in India as he is not selfish. You will have to wait soon we will cross USA. #COVID__19 https://twitter.com/aabidmagami/status/1291350141186670593 …0320NaN4852143151483.0455.60000017297['be', 'for', 'in', 'and', 'in', 'did', 'not', 'in', 'as', 'he', 'is', 'not', 'will', 'have', 'to', 'we', 'will']modiji honoured covid memorial prize highest covid cases india helping countries fighting corona fight india selfish wait soon cross covid__-0.250000The Hill/HarrisXThe Hill2020-08-022020-08-052850 RV1.843.040.03.06Good Response
17475174751596672000000000000carman17838926THANKS BE TO TRUMP AND THE WEALTHY GOP SENATORS WHO ABANDONED THESE POOR FOLKS, ESPECIALLY INCLUDING THEIR OWN MILLIONAIRE SENATORS CASSIDY AND KENNEDY😩\nThe rare case of a state ravaged twice by COVID-19 — USA TODAY0000NaN4852143151483.0365.0000003216['of', 'a', 'by']thanks trump wealthy senators abandoned poor folks especially including millionaire senators cassidy kennedy rare case state ravaged twice covid today0.120000The Hill/HarrisXThe Hill2020-08-022020-08-052850 RV1.843.040.03.06Good Response
17476174761596672000000000000renterialawfirmUSA TODAY: 'We're in for a bad and rocky ride:' Ex-WHO doctor who helped eradicate smallpox predicts COVID-19 turmoil for years.\n https://www.usatoday.com/story/news/health/2020/08/03/covid-19-us-who-doctor-larry-brilliant/5574854002/ …0000NaN4852143151483.0229.2608706236['in', 'for', 'a', 'and', 'who', 'for']today rocky ride exwho doctor helped eradicate smallpox predicts covid turmoil years0.000000The Hill/HarrisXThe Hill2020-08-022020-08-052850 RV1.843.040.03.06Good Response
17477174771596672000000000000squarerootal2Places where Covid-19 thrives and people are dying - USA. Places where it has been flattened and deaths are low- Europe, Scandinavia, China, Australia, NZ. Why? Our President has no national plan and his comrades like @Jim_Jordan think that’s ok.2030NaN4852143151483.0405.17500013246['where', 'and', 'are', 'where', 'it', 'has', 'been', 'and', 'are', 'has', 'no', 'and', 'his']places covid thrives people dying places flattened deaths europe scandinavia china australia president national plan comrades like jim_jordan think thats0.000000The Hill/HarrisXThe Hill2020-08-022020-08-052850 RV1.843.040.03.06Good Response
17478174781596672000000000000miasruleAgreed. And let’s be clear, taking the current Covid-19 disaster out of it, it’s not just our fellow USA humans we are good at killing. Violence is American as apple pie.1000NaN4852143151483.0314.51612912170['be', 'the', 'out', 'of', 'not', 'just', 'our', 'we', 'are', 'at', 'is', 'as']agreed lets clear taking current covid disaster fellow humans good killing violence american apple0.200000The Hill/HarrisXThe Hill2020-08-022020-08-052850 RV1.843.040.03.06Good Response
17479174791596672000000000000lindalouwhohWhen it comes to Covid-19, USA resembles not wealthy & powerful countries but instead far poorer countries, like Brazil, Peru and South Africa, or those with large migrant populations, like Bahrain and Oman\n\nThe Unique U.S. Failure to Control the Virus https://nyti.ms/3kfpzvj0000NaN4852143151483.0425.57142911278['it', 'to', 'not', 'but', 'and', 'or', 'those', 'with', 'and', 'to', 'the']comes covid resembles wealthy powerful countries instead poorer countries like brazil peru south africa large migrant populations like bahrain oman unique failure control virus0.214524The Hill/HarrisXThe Hill2020-08-022020-08-052850 RV1.843.040.03.06Good Response
17480174801596672000000000000acai_wWoW, such a bold and honest statement and brought to you with a lot of integrity .... Please give us an update of the Covid-19 spread in the country and how the USA is handling it? Give us an update on to whom the Bailouts are being paid out and why? Etc. etc. https://twitter.com/thehill/status/1291018874259791872 …0000NaN4852143151483.0554.78181827318['such', 'a', 'and', 'and', 'to', 'you', 'with', 'a', 'of', 'an', 'of', 'the', 'in', 'the', 'and', 'how', 'the', 'is', 'an', 'on', 'to', 'whom', 'the', 'are', 'being', 'out', 'and']bold honest statement brought integrity please give update covid spread country handling give update bailouts paid0.466667The Hill/HarrisXThe Hill2020-08-022020-08-052850 RV1.843.040.03.06Good Response